Week 8: Merging, Maps, and More
Factors
Factors…
Factors are a specific way to store categorical data. Usign factors results in a more efficient data storage process, but can be cumbersome.
Factors can be necessary for making plots and fitting models in R.
Character Values
## [1] "character"## [1] "Friday" "Saturday" "Sunday"Creating Factors
## [1] "Friday" "Monday" "Saturday" "Sunday"## [1] "factor"## [1] Friday Monday Saturday Sunday
## Levels: Friday Monday Saturday SundayCreating Factors
Rather than coercing a class variable to be a factor, the factor can be created directly.
day_factor2 <- factor(c('Friday', 'Saturday', 'Sunday', 'Monday'),
levels = c('Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday'))
sort(day_factor2)## [1] Sunday Monday Friday Saturday
## Levels: Sunday Monday Tuesday Wednesday Thursday Friday SaturdaySort / Order / Arrange
Sort
We have seen the sort command, this returns a sorted result.
ski_acres <- data.frame( ski.hill = c('Big Sky','Bridger Bowl', 'Jackson', 'Steamboat' ),
skiable.acres = c(5800,2000, "2500+",2965))
kable(ski_acres)| ski.hill | skiable.acres |
|---|---|
| Big Sky | 5800 |
| Bridger Bowl | 2000 |
| Jackson | 2500+ |
| Steamboat | 2965 |
Exercise:
Q1.
What type of variable is skiable.acres in the ski_acres data frame?
Q2.
Sort the ski resorts by skiable acres in descending fashion.
Solution:
Q1.
What type of variable is skiable.acres in the ski_acres data frame?
## [1] "factor"Solution:
Q2.
Sort the ski resorts by skiable acres in descending fashion.
## ski.hill skiable.acres
## 1 Big Sky 5800
## 2 Steamboat 2965
## 3 Jackson 2500+
## 4 Bridger Bowl 2000arrange
Arrange is one of the verb words in the dplyr() package and can be very useful for arranging data frames.
sort/order
Otherwise, the sort and order functions, in base R, can be used.
## [1] 2 3 4 1## ski.hill skiable.acres
## 1 Big Sky 5800
## 4 Steamboat 2965
## 3 Jackson 2500+
## 2 Bridger Bowl 2000categorical variables and factors
We have already seen sorting on factor variables, what about plain categorical variables?
## [1] "Friday" "Monday" "Saturday" "Sunday"## [1] "1" "10" "2" "3"Merge / Join
Merge
Another important skill is merging or combining data sets.
Consider the two data frames, how can we merge them and what should be the dimensions of the merged data frame.
| school | state |
|---|---|
| MSU | MT |
| VT | VA |
| Mines | CO |
| school | enrollment |
|---|---|
| Mines | 5794 |
| MSU | 15688 |
| VT | 30598 |
pre-sort
One possibility is to use the arrange the data frames first.
pre-sort
One possibility is to use the arrange the data frames first.
## school state
## 3 Mines CO
## 1 MSU MT
## 2 VT VA## school enrollment
## 1 Mines 5794
## 2 MSU 15688
## 3 VT 30598rbind() and cbind()
Now, given that the data frames are both sorted the same way, we can bind the rows together.
## school state school enrollment
## 3 Mines CO Mines 5794
## 1 MSU MT MSU 15688
## 2 VT VA VT 30598rbind() and cbind()
Now assume we want to add another school to the data frame.
## school state enrollment
## 3 Mines CO 5794
## 1 MSU MT 15688
## 2 VT VA 30598
## 4 Luther IA 2337Note: if your strings are saved as factors, this chunk of code will give you an error.
bind_rows() / bind_cols()
dplyr also contains functions for binding rows and columns.
Exercise
Now combine the following information into a single table sorted alphabetically by the name of the ski hill.
## ski.hill skiable.acres
## 1 Big Sky 5800
## 2 Bridger Bowl 2000
## 3 Jackson 2500+
## 4 Steamboat 2965## ski.resort ticket.cost
## 1 Bridger Bowl 60
## 2 Big Sky depends
## 3 Steamboat 145
## 4 Jackson 130## [1] "Discovery" "2200" "20"Solution part 1
Now combine the following information into a single table sorted alphabetically by the name of the ski hill.
## ski.hill skiable.acres
## 1 Big Sky 5800
## 2 Bridger Bowl 2000
## 3 Jackson 2500+
## 4 Steamboat 2965## 'data.frame': 4 obs. of 3 variables:
## $ ski.hill : Factor w/ 4 levels "Big Sky","Bridger Bowl",..: 1 2 3 4
## $ skiable.acres: Factor w/ 4 levels "2000","2500+",..: 4 1 2 3
## $ ticket.cost : Factor w/ 4 levels "130","145","60",..: 4 3 1 2Solution part 2
df_comb <- df_comb %>% mutate(ski.hill = as.character(ski.hill),
skiable.acres = as.character(skiable.acres),
ticket.cost = as.character(ticket.cost))
str(df_comb)## 'data.frame': 4 obs. of 3 variables:
## $ ski.hill : chr "Big Sky" "Bridger Bowl" "Jackson" "Steamboat"
## $ skiable.acres: chr "5800" "2000" "2500+" "2965"
## $ ticket.cost : chr "depends" "60" "130" "145"Solution part 3
## ski.hill skiable.acres ticket.cost
## 1 Big Sky 5800 depends
## 2 Bridger Bowl 2000 60
## 3 Discovery 2200 20
## 4 Jackson 2500+ 130
## 5 Steamboat 2965 145join()
We could have also used some of the more advanced merge (join) features from dplyr.
## school state enrollment
## 1 Mines CO 5794
## 2 MSU MT 15688
## 3 VT VA 30598more about join
The dplyr join command includes the following options:
- left join
- right join
- inner join
- full join
- anti join
Exercise
Combine the two data sets
## ski.hill skiable.acres
## 1 Big Sky 5800
## 2 Bridger Bowl 2000
## 3 Jackson 2500+
## 4 Steamboat 2965## ski.resort ticket.cost
## 1 Big Sky depends
## 2 Bridger Bowl 60
## 3 Jackson 130
## 4 Steamboat 145## # A tibble: 1 x 3
## ski.hill skiable.acres ticket.cost
## <chr> <chr> <chr>
## 1 Discovery 2200 20Solution
full_join(ski_acres, df_cost, by = c("ski.hill"= "ski.resort")) %>%
mutate(ski.hill = as.character(ski.hill),
skiable.acres = as.character(skiable.acres),
ticket.cost = as.character(ticket.cost)) %>%
full_join(disco_df) %>%
arrange(ski.hill)## Joining, by = c("ski.hill", "skiable.acres", "ticket.cost")## ski.hill skiable.acres ticket.cost
## 1 Big Sky 5800 depends
## 2 Bridger Bowl 2000 60
## 3 Discovery 2200 20
## 4 Jackson 2500+ 130
## 5 Steamboat 2965 145wide / long data
Hoops Data
This is a wide dataset
hoops <- read_csv('http://www.math.montana.edu/ahoegh/teaching/stat408/datasets/TourneyDetailedResults.csv')
head(hoops)## # A tibble: 6 x 34
## Season Daynum Wteam Wscore Lteam Lscore Wloc Numot Wfgm Wfga Wfgm3 Wfga3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2003 134 1421 92 1411 84 N 1 32 69 11 29
## 2 2003 136 1112 80 1436 51 N 0 31 66 7 23
## 3 2003 136 1113 84 1272 71 N 0 31 59 6 14
## 4 2003 136 1141 79 1166 73 N 0 29 53 3 7
## 5 2003 136 1143 76 1301 74 N 1 27 64 7 20
## 6 2003 136 1163 58 1140 53 N 0 17 52 4 14
## # … with 22 more variables: Wftm <dbl>, Wfta <dbl>, Wor <dbl>, Wdr <dbl>,
## # Wast <dbl>, Wto <dbl>, Wstl <dbl>, Wblk <dbl>, Wpf <dbl>, Lfgm <dbl>,
## # Lfga <dbl>, Lfgm3 <dbl>, Lfga3 <dbl>, Lftm <dbl>, Lfta <dbl>, Lor <dbl>,
## # Ldr <dbl>, Last <dbl>, Lto <dbl>, Lstl <dbl>, Lblk <dbl>, Lpf <dbl>Plot Creation
Consider a plot where the number of points is colored or faceted by winning or losing.
This requires a dataset that is thin and looks something like
| Game Id | Points | win/lose |
|---|---|---|
| 1 | 92 | win |
| 1 | 84 | lose |
| 2 | 80 | win |
| 2 | 51 | lose |
Data Wrangling
lose_score <- hoops %>% select(score = Lscore) %>%
mutate(outcome = 'lose', game_id = row_number())
win_score <- hoops %>% select(score = Wscore) %>%
mutate(outcome = 'win', game_id = row_number())
thin_scores <- bind_rows(win_score, lose_score) %>%
arrange(game_id)
thin_scores## # A tibble: 1,828 x 3
## score outcome game_id
## <dbl> <chr> <int>
## 1 92 win 1
## 2 84 lose 1
## 3 80 win 2
## 4 51 lose 2
## 5 84 win 3
## 6 71 lose 3
## 7 79 win 4
## 8 73 lose 4
## 9 76 win 5
## 10 74 lose 5
## # … with 1,818 more rowsPlot Creation
tidyr package
The tidyr package contains additional tools for converting files from long to wide formats.
Consider the billboard dataset which contains the rank of the song (in 2000) for each week after it first entered the list.
## # A tibble: 317 x 79
## artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
## <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
## 2 2Ge+h… The … 2000-09-02 91 87 92 NA NA NA NA NA
## 3 3 Doo… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
## 4 3 Doo… Loser 2000-10-21 76 76 72 69 67 65 55 59
## 5 504 B… Wobb… 2000-04-15 57 34 25 17 17 31 36 49
## 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
## 7 A*Tee… Danc… 2000-07-08 97 97 96 95 100 NA NA NA
## 8 Aaliy… I Do… 2000-01-29 84 62 51 41 38 35 35 38
## 9 Aaliy… Try … 2000-03-18 59 53 38 28 21 18 16 14
## 10 Adams… Open… 2000-08-26 76 76 74 69 68 67 61 58
## # … with 307 more rows, and 68 more variables: wk9 <dbl>, wk10 <dbl>,
## # wk11 <dbl>, wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>,
## # wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>,
## # wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, wk27 <dbl>, wk28 <dbl>,
## # wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, wk32 <dbl>, wk33 <dbl>, wk34 <dbl>,
## # wk35 <dbl>, wk36 <dbl>, wk37 <dbl>, wk38 <dbl>, wk39 <dbl>, wk40 <dbl>,
## # wk41 <dbl>, wk42 <dbl>, wk43 <dbl>, wk44 <dbl>, wk45 <dbl>, wk46 <dbl>,
## # wk47 <dbl>, wk48 <dbl>, wk49 <dbl>, wk50 <dbl>, wk51 <dbl>, wk52 <dbl>,
## # wk53 <dbl>, wk54 <dbl>, wk55 <dbl>, wk56 <dbl>, wk57 <dbl>, wk58 <dbl>,
## # wk59 <dbl>, wk60 <dbl>, wk61 <dbl>, wk62 <dbl>, wk63 <dbl>, wk64 <dbl>,
## # wk65 <dbl>, wk66 <lgl>, wk67 <lgl>, wk68 <lgl>, wk69 <lgl>, wk70 <lgl>,
## # wk71 <lgl>, wk72 <lgl>, wk73 <lgl>, wk74 <lgl>, wk75 <lgl>, wk76 <lgl>billboard data
If we want to identify songs that reach number 1 quickly, the data needs to manipulated.
billboard %>%
select(artist, track, date.entered, wk1, wk2) %>%
pivot_longer(
cols= c('wk1', 'wk2'),
names_to = 'week',
values_to = 'rank',
values_drop_na = T)## # A tibble: 629 x 5
## artist track date.entered week rank
## <chr> <chr> <date> <chr> <dbl>
## 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
## 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
## 3 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
## 4 2Ge+her The Hardest Part Of ... 2000-09-02 wk2 87
## 5 3 Doors Down Kryptonite 2000-04-08 wk1 81
## 6 3 Doors Down Kryptonite 2000-04-08 wk2 70
## 7 3 Doors Down Loser 2000-10-21 wk1 76
## 8 3 Doors Down Loser 2000-10-21 wk2 76
## 9 504 Boyz Wobble Wobble 2000-04-15 wk1 57
## 10 504 Boyz Wobble Wobble 2000-04-15 wk2 34
## # … with 619 more rowsBillboard Data Analysis
billboard %>%
pivot_longer(
cols= starts_with('wk'),
names_to = 'week',
values_to = 'rank',
values_drop_na = T) %>%
mutate(week_numb= as.numeric(str_replace(week, 'wk',''))) %>%
filter(rank == 1) %>%
arrange(week_numb) %>%
slice(1) %>%
kable()| artist | track | date.entered | week | rank | week_numb |
|---|---|---|---|---|---|
| Madonna | Music | 2000-08-12 | wk6 | 1 | 6 |
Exercise
Determine which song in this dataset spent the most time at #1
Solution
billboard %>%
pivot_longer(
cols= starts_with('wk'),
names_to = 'week',
values_to = 'rank',
values_drop_na = T) %>%
mutate(week_numb= as.numeric(str_replace(week, 'wk',''))) %>%
filter(rank == 1) %>%
group_by(track) %>%
tally() %>%
arrange(desc(n)) %>%
kable()| track | n |
|---|---|
| Independent Women Pa… | 11 |
| Maria, Maria | 10 |
| Come On Over Baby (A… | 4 |
| I Knew I Loved You | 4 |
| Music | 4 |
| Be With You | 3 |
| Doesn’t Really Matte… | 3 |
| Say My Name | 3 |
| Amazed | 2 |
| Incomplete | 2 |
| It’s Gonna Be Me | 2 |
| What A Girl Wants | 2 |
| Bent | 1 |
| Everything You Want | 1 |
| Thank God I Found Yo… | 1 |
| Try Again | 1 |
| With Arms Wide Open | 1 |
pivot_wider()
The pivot_wider() function allows transformation in the other direction from thin to wide.
Maps in R
ggplot functionality
mt_counties <- map_data("county", "montana") %>%
select(lon = long, lat, group, id = subregion)
class(mt_counties)## [1] "data.frame"## lon lat group id
## 1 -113.5373 45.90538 1 beaverhead
## 2 -113.4915 45.88246 1 beaverhead
## 3 -113.4399 45.84235 1 beaverhead
## 4 -113.3826 45.74495 1 beaverhead
## 5 -113.3540 45.76214 1 beaverhead
## 6 -113.3482 45.80798 1 beaverhead
## 7 -113.2566 45.82516 1 beaverhead
## 8 -113.2566 45.85381 1 beaverhead
## 9 -113.1935 45.88246 1 beaverhead
## 10 -113.1362 45.88246 1 beaverhead
## 11 -113.0961 45.86527 1 beaverhead
## 12 -113.0732 45.85381 1 beaverhead
## 13 -112.9930 45.80225 1 beaverhead
## 14 -112.9071 45.79079 1 beaverhead
## 15 -112.7696 45.75641 1 beaverhead
## 16 -112.7352 45.68192 1 beaverhead
## 17 -112.6893 45.65900 1 beaverhead
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## 28 -112.3284 45.08605 1 beaverhead
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## 30 -112.1851 45.00583 1 beaverhead
## 31 -112.1794 44.91416 1 beaverhead
## 32 -112.1622 44.90843 1 beaverhead
## 33 -112.1622 44.82822 1 beaverhead
## 34 -112.0591 44.82822 1 beaverhead
## 35 -112.0591 44.78238 1 beaverhead
## 36 -111.7783 44.78238 1 beaverhead
## 37 -111.7783 44.71936 1 beaverhead
## 38 -111.4804 44.71936 1 beaverhead
## 39 -111.4804 44.68498 1 beaverhead
## 40 -111.4976 44.66206 1 beaverhead
## 41 -111.5148 44.62195 1 beaverhead
## 42 -111.4689 44.55320 1 beaverhead
## 43 -111.4804 44.54747 1 beaverhead
## 44 -111.5835 44.56466 1 beaverhead
## 45 -111.6236 44.56466 1 beaverhead
## 46 -111.6580 44.55320 1 beaverhead
## 47 -111.8070 44.52455 1 beaverhead
## 48 -111.8643 44.56466 1 beaverhead
## 49 -112.0591 44.53601 1 beaverhead
## 50 -112.1106 44.54174 1 beaverhead
## 51 -112.1737 44.53028 1 beaverhead
## 52 -112.2424 44.57611 1 beaverhead
## 53 -112.3398 44.53601 1 beaverhead
## 54 -112.3456 44.46152 1 beaverhead
## 55 -112.4029 44.45007 1 beaverhead
## 56 -112.4487 44.47871 1 beaverhead
## 57 -112.7180 44.50736 1 beaverhead
## 58 -112.7925 44.47298 1 beaverhead
## 59 -112.8211 44.42142 1 beaverhead
## 60 -112.8154 44.38131 1 beaverhead
## 61 -112.8440 44.36985 1 beaverhead
## 62 -112.8956 44.40996 1 beaverhead
## 63 -112.9644 44.42142 1 beaverhead
## 64 -113.0217 44.47871 1 beaverhead
## 65 -113.0159 44.51882 1 beaverhead
## 66 -113.0789 44.60477 1 beaverhead
## 67 -113.0560 44.63914 1 beaverhead
## 68 -113.1362 44.76519 1 beaverhead
## 69 -113.2279 44.82249 1 beaverhead
## 70 -113.3139 44.79384 1 beaverhead
## 71 -113.3540 44.78238 1 beaverhead
## 72 -113.3597 44.81103 1 beaverhead
## 73 -113.3883 44.82822 1 beaverhead
## 74 -113.4456 44.86832 1 beaverhead
## 75 -113.4915 44.93708 1 beaverhead
## 76 -113.4399 44.96573 1 beaverhead
## 77 -113.4456 45.05740 1 beaverhead
## 78 -113.5144 45.12043 1 beaverhead
## 79 -113.5717 45.13189 1 beaverhead
## 80 -113.6977 45.29804 1 beaverhead
## 81 -113.7207 45.35534 1 beaverhead
## 82 -113.7608 45.45847 1 beaverhead
## 83 -113.7493 45.49285 1 beaverhead
## 84 -113.7608 45.52150 1 beaverhead
## 85 -113.8181 45.54441 1 beaverhead
## 86 -113.7894 45.58452 1 beaverhead
## 87 -113.8066 45.61317 1 beaverhead
## 88 -113.8639 45.63036 1 beaverhead
## 89 -113.8811 45.64182 1 beaverhead
## 90 -113.9327 45.69911 1 beaverhead
## 91 -113.8925 45.72776 1 beaverhead
## 92 -113.8811 45.75068 1 beaverhead
## 93 -113.8524 45.75068 1 beaverhead
## 94 -113.8066 45.74495 1 beaverhead
## 95 -113.7837 45.76787 1 beaverhead
## 96 -113.7722 45.80798 1 beaverhead
## 97 -113.7379 45.83089 1 beaverhead
## 98 -113.6920 45.85381 1 beaverhead
## 99 -113.5946 45.88246 1 beaverhead
## 100 -113.5373 45.90538 1 beaverhead
## 102 -106.2550 45.18918 2 big horn
## 103 -106.2321 45.18918 2 big horn
## 104 -106.2378 45.00583 2 big horn
## 105 -106.3467 45.00583 2 big horn
## 106 -106.6905 45.00583 2 big horn
## 107 -107.0285 45.00583 2 big horn
## 108 -107.3322 45.00583 2 big horn
## 109 -107.5843 45.00010 2 big horn
## 110 -107.7791 45.00010 2 big horn
## 111 -107.9051 44.99438 2 big horn
## 112 -108.2833 45.00010 2 big horn
## 113 -108.2375 45.07459 2 big horn
## 114 -108.2031 45.10897 2 big horn
## 115 -108.1802 45.15480 2 big horn
## 116 -108.0942 45.22356 2 big horn
## 117 -108.7073 45.22356 2 big horn
## 118 -108.7073 45.36107 2 big horn
## 119 -108.6844 45.40118 2 big horn
## 120 -108.6844 45.41837 2 big horn
## 121 -108.6901 45.43555 2 big horn
## 122 -108.6557 45.46993 2 big horn
## 123 -108.4380 45.46420 2 big horn
## 124 -108.3177 45.46993 2 big horn
## 125 -108.3177 45.49285 2 big horn
## 126 -108.2031 45.49285 2 big horn
## 127 -108.1973 45.52150 2 big horn
## 128 -108.0656 45.52150 2 big horn
## 129 -108.0656 45.78506 2 big horn
## 130 -108.0426 45.79079 2 big horn
## 131 -108.0369 45.89392 2 big horn
## 132 -107.8822 45.90538 2 big horn
## 133 -107.8765 45.93402 2 big horn
## 134 -107.8364 45.93976 2 big horn
## 135 -107.8307 45.96840 2 big horn
## 136 -107.7848 45.96268 2 big horn
## 137 -107.7848 45.97986 2 big horn
## 138 -107.6702 45.98560 2 big horn
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## 140 -107.5098 46.04289 2 big horn
## 141 -107.4181 46.04289 2 big horn
## 142 -107.4124 45.95694 2 big horn
## 143 -107.1718 45.93976 2 big horn
## 144 -107.1660 45.91684 2 big horn
## 145 -107.0400 45.91684 2 big horn
## 146 -107.0342 45.86527 2 big horn
## 147 -106.9311 45.86527 2 big horn
## 148 -106.9254 45.82516 2 big horn
## 149 -106.8681 45.82516 2 big horn
## 150 -106.8681 45.79079 2 big horn
## 151 -106.9082 45.78506 2 big horn
## 152 -106.9082 45.68192 2 big horn
## 153 -106.8566 45.68192 2 big horn
## 154 -106.8452 45.67047 2 big horn
## 155 -106.7306 45.67620 2 big horn
## 156 -106.7306 45.35534 2 big horn
## 157 -106.7707 45.34961 2 big horn
## 158 -106.7707 45.18918 2 big horn
## 159 -106.2550 45.18918 2 big horn
## 161 -108.8964 47.73884 3 blaine
## 162 -108.9422 47.73312 3 blaine
## 163 -108.9537 47.75031 3 blaine
## 164 -108.9307 47.76749 3 blaine
## 165 -108.9422 47.80187 3 blaine
## 166 -109.0052 47.81333 3 blaine
## 167 -109.0511 47.79614 3 blaine
## 168 -109.0912 47.80187 3 blaine
## 169 -109.1370 47.78468 3 blaine
## 170 -109.2058 47.80187 3 blaine
## 171 -109.2573 47.79614 3 blaine
## 172 -109.3834 47.72739 3 blaine
## 173 -109.4464 47.72739 3 blaine
## 174 -109.5495 47.74457 3 blaine
## 175 -109.5495 47.85917 3 blaine
## 176 -109.5323 47.85917 3 blaine
## 177 -109.5323 48.01386 3 blaine
## 178 -109.5323 48.12845 3 blaine
## 179 -109.5266 48.20294 3 blaine
## 180 -109.5495 48.22013 3 blaine
## 181 -109.5495 48.28315 3 blaine
## 182 -109.5209 48.28315 3 blaine
## 183 -109.5209 48.38629 3 blaine
## 184 -109.4922 48.39202 3 blaine
## 185 -109.4922 48.44358 3 blaine
## 186 -109.4579 48.44358 3 blaine
## 187 -109.4579 48.51807 3 blaine
## 188 -109.4865 48.51807 3 blaine
## 189 -109.5037 48.56390 3 blaine
## 190 -109.4980 48.90768 3 blaine
## 191 -109.4750 48.91341 3 blaine
## 192 -109.4750 48.99362 3 blaine
## 193 -109.3146 48.99362 3 blaine
## 194 -109.0740 48.99362 3 blaine
## 195 -108.7932 48.99362 3 blaine
## 196 -108.5010 48.99362 3 blaine
## 197 -108.2317 48.99362 3 blaine
## 198 -108.2375 48.91914 3 blaine
## 199 -108.2604 48.91341 3 blaine
## 200 -108.2604 48.74152 3 blaine
## 201 -108.3177 48.73006 3 blaine
## 202 -108.3119 48.56390 3 blaine
## 203 -108.3406 48.56390 3 blaine
## 204 -108.3521 48.43213 3 blaine
## 205 -108.4093 48.42066 3 blaine
## 206 -108.4151 48.21440 3 blaine
## 207 -108.4380 48.21440 3 blaine
## 208 -108.4380 47.98521 3 blaine
## 209 -108.5526 47.98521 3 blaine
## 210 -108.5984 47.99667 3 blaine
## 211 -108.6213 47.98521 3 blaine
## 212 -108.6385 47.95084 3 blaine
## 213 -108.6328 47.92219 3 blaine
## 214 -108.8849 47.91646 3 blaine
## 215 -108.8849 47.85917 3 blaine
## 216 -108.8964 47.84771 3 blaine
## 217 -108.8964 47.73884 3 blaine
## 219 -111.7841 46.56428 4 broadwater
## 220 -111.6408 46.57001 4 broadwater
## 221 -111.6466 46.74763 4 broadwater
## 222 -111.6007 46.77628 4 broadwater
## 223 -111.5492 46.76482 4 broadwater
## 224 -111.5148 46.75909 4 broadwater
## 225 -111.4804 46.77054 4 broadwater
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## 1696 -114.5858 46.62730 32 missoula
## 1697 -114.6317 46.64450 32 missoula
## 1698 -114.6317 46.67314 32 missoula
## 1699 -114.6374 46.71325 32 missoula
## 1700 -114.6660 46.73617 32 missoula
## 1701 -114.5572 46.73617 32 missoula
## 1702 -114.5515 46.82211 32 missoula
## 1703 -114.5686 46.82784 32 missoula
## 1704 -114.5744 46.94816 32 missoula
## 1705 -114.5457 46.95389 32 missoula
## 1706 -114.5515 46.98254 32 missoula
## 1707 -114.5228 46.98827 32 missoula
## 1708 -114.5228 46.99400 32 missoula
## 1709 -114.4082 46.99400 32 missoula
## 1710 -114.4139 47.01119 32 missoula
## 1711 -114.4770 47.01691 32 missoula
## 1712 -114.4827 47.04556 32 missoula
## 1713 -114.5572 47.05130 32 missoula
## 1714 -114.5572 47.07994 32 missoula
## 1715 -114.5916 47.07994 32 missoula
## 1716 -114.5801 47.10859 32 missoula
## 1717 -114.6202 47.10286 32 missoula
## 1718 -114.6202 47.12578 32 missoula
## 1719 -114.6488 47.12005 32 missoula
## 1720 -114.6431 47.14870 32 missoula
## 1721 -114.6832 47.15443 32 missoula
## 1722 -114.6890 47.21172 32 missoula
## 1723 -114.7635 47.21745 32 missoula
## 1724 -114.7692 47.23464 32 missoula
## 1725 -114.7864 47.23464 32 missoula
## 1726 -114.7864 47.26329 32 missoula
## 1727 -114.6890 47.25756 32 missoula
## 1728 -114.6488 47.27475 32 missoula
## 1729 -114.5515 47.25183 32 missoula
## 1730 -114.5113 47.25756 32 missoula
## 1731 -114.4712 47.21745 32 missoula
## 1732 -114.3452 47.18880 32 missoula
## 1733 -114.3337 47.14870 32 missoula
## 1734 -114.2707 47.11432 32 missoula
## 1735 -114.2363 47.12578 32 missoula
## 1736 -114.2077 47.12005 32 missoula
## 1737 -114.1676 47.13724 32 missoula
## 1738 -113.9212 47.13151 32 missoula
## 1739 -113.9155 47.16589 32 missoula
## 1740 -113.8123 47.17162 32 missoula
## 1741 -113.8123 47.26329 32 missoula
## 1742 -113.8925 47.29193 32 missoula
## 1743 -113.8582 47.31485 32 missoula
## 1744 -113.8754 47.37788 32 missoula
## 1745 -113.8582 47.42372 32 missoula
## 1746 -113.8754 47.43518 32 missoula
## 1747 -113.9327 47.45810 32 missoula
## 1748 -113.9670 47.56123 32 missoula
## 1749 -113.9556 47.59560 32 missoula
## 1750 -113.7379 47.58987 32 missoula
## 1751 -113.6061 47.58987 32 missoula
## 1753 -107.8249 46.74763 33 musselshell
## 1754 -107.8135 46.74190 33 musselshell
## 1755 -107.8307 46.71898 33 musselshell
## 1756 -107.7848 46.69033 33 musselshell
## 1757 -107.8364 46.61012 33 musselshell
## 1758 -107.7906 46.59866 33 musselshell
## 1759 -107.7906 46.50126 33 musselshell
## 1760 -107.7791 46.48980 33 musselshell
## 1761 -107.7791 46.38666 33 musselshell
## 1762 -107.9338 46.39240 33 musselshell
## 1763 -107.9338 46.37521 33 musselshell
## 1764 -107.9510 46.37521 33 musselshell
## 1765 -107.9510 46.35801 33 musselshell
## 1766 -107.9682 46.35801 33 musselshell
## 1767 -107.9682 46.34082 33 musselshell
## 1768 -107.9854 46.34082 33 musselshell
## 1769 -107.9854 46.32937 33 musselshell
## 1770 -108.0140 46.34082 33 musselshell
## 1771 -108.0140 46.30645 33 musselshell
## 1772 -108.0312 46.30645 33 musselshell
## 1773 -108.0312 46.26061 33 musselshell
## 1774 -108.3177 46.25488 33 musselshell
## 1775 -108.3177 46.24342 33 musselshell
## 1776 -108.3635 46.24342 33 musselshell
## 1777 -108.3635 46.22623 33 musselshell
## 1778 -108.4036 46.22623 33 musselshell
## 1779 -108.4093 46.12883 33 musselshell
## 1780 -108.7818 46.13456 33 musselshell
## 1781 -108.7760 46.27207 33 musselshell
## 1782 -108.7990 46.27207 33 musselshell
## 1783 -108.7990 46.28353 33 musselshell
## 1784 -108.8161 46.30072 33 musselshell
## 1785 -108.8161 46.30645 33 musselshell
## 1786 -108.8333 46.31218 33 musselshell
## 1787 -108.8276 46.33510 33 musselshell
## 1788 -108.8448 46.35801 33 musselshell
## 1789 -108.8677 46.35801 33 musselshell
## 1790 -108.8562 46.43823 33 musselshell
## 1791 -108.8792 46.44396 33 musselshell
## 1792 -108.8849 46.47834 33 musselshell
## 1793 -108.9078 46.48407 33 musselshell
## 1794 -108.9078 46.56428 33 musselshell
## 1795 -108.9766 46.56428 33 musselshell
## 1796 -108.9766 46.61012 33 musselshell
## 1797 -109.0052 46.61584 33 musselshell
## 1798 -108.9995 46.74763 33 musselshell
## 1799 -108.6271 46.74763 33 musselshell
## 1800 -107.8249 46.74763 33 musselshell
## 1802 -110.0537 45.17772 34 park
## 1803 -109.8131 45.17199 34 park
## 1804 -109.8131 44.99438 34 park
## 1805 -109.9964 44.99438 34 park
## 1806 -109.9850 45.01157 34 park
## 1807 -109.9735 45.04021 34 park
## 1808 -110.0022 45.05167 34 park
## 1809 -110.0308 45.05167 34 park
## 1810 -110.0308 45.04021 34 park
## 1811 -110.1225 45.02875 34 park
## 1812 -110.5236 45.03448 34 park
## 1813 -110.8043 45.03448 34 park
## 1814 -110.8158 45.05167 34 park
## 1815 -110.8387 45.06313 34 park
## 1816 -110.8731 45.04594 34 park
## 1817 -110.8673 45.02875 34 park
## 1818 -110.9189 45.03448 34 park
## 1819 -110.9304 45.06313 34 park
## 1820 -110.9590 45.07459 34 park
## 1821 -110.9647 45.09751 34 park
## 1822 -110.9991 45.08605 34 park
## 1823 -111.0163 45.07459 34 park
## 1824 -111.0450 45.06313 34 park
## 1825 -111.0450 45.21783 34 park
## 1826 -111.0220 45.22356 34 park
## 1827 -111.0163 45.34388 34 park
## 1828 -110.9132 45.34961 34 park
## 1829 -110.9132 45.52723 34 park
## 1830 -110.8616 45.52723 34 park
## 1831 -110.8616 45.59025 34 park
## 1832 -110.8043 45.59025 34 park
## 1833 -110.8043 46.14029 34 park
## 1834 -110.7871 46.14602 34 park
## 1835 -110.7928 46.19186 34 park
## 1836 -110.4262 46.19759 34 park
## 1837 -110.4147 46.18040 34 park
## 1838 -110.2829 46.18040 34 park
## 1839 -110.2829 46.14029 34 park
## 1840 -110.3058 46.13456 34 park
## 1841 -110.3001 45.79079 34 park
## 1842 -110.2142 45.78506 34 park
## 1843 -110.2084 45.36107 34 park
## 1844 -110.2314 45.33815 34 park
## 1845 -110.2256 45.17772 34 park
## 1846 -110.0537 45.17772 34 park
## 1848 -107.8765 46.84503 35 petroleum
## 1849 -107.8249 46.74763 35 petroleum
## 1850 -108.6271 46.74763 35 petroleum
## 1851 -108.6328 46.83357 35 petroleum
## 1852 -108.6041 46.83930 35 petroleum
## 1853 -108.6099 47.08567 35 petroleum
## 1854 -108.7245 47.09713 35 petroleum
## 1855 -108.7359 47.17735 35 petroleum
## 1856 -108.7130 47.18880 35 petroleum
## 1857 -108.7130 47.26902 35 petroleum
## 1858 -108.5870 47.26902 35 petroleum
## 1859 -108.5870 47.29193 35 petroleum
## 1860 -108.3406 47.30340 35 petroleum
## 1861 -108.3119 47.32059 35 petroleum
## 1862 -108.3234 47.57269 35 petroleum
## 1863 -108.3119 47.58987 35 petroleum
## 1864 -108.2145 47.59560 35 petroleum
## 1865 -108.0770 47.60133 35 petroleum
## 1866 -108.0140 47.60133 35 petroleum
## 1867 -107.9624 47.58987 35 petroleum
## 1868 -107.9223 47.56123 35 petroleum
## 1869 -107.9051 47.49820 35 petroleum
## 1870 -107.9166 47.45810 35 petroleum
## 1871 -107.9223 47.44091 35 petroleum
## 1872 -107.8994 47.41226 35 petroleum
## 1873 -107.9109 47.38934 35 petroleum
## 1874 -107.9510 47.37215 35 petroleum
## 1875 -107.9682 47.34923 35 petroleum
## 1876 -107.9395 47.33204 35 petroleum
## 1877 -107.9510 47.20026 35 petroleum
## 1878 -107.9281 47.18307 35 petroleum
## 1879 -107.9624 47.16016 35 petroleum
## 1880 -107.9624 47.13724 35 petroleum
## 1881 -107.9166 47.04556 35 petroleum
## 1882 -107.9223 47.02264 35 petroleum
## 1883 -107.8880 47.01691 35 petroleum
## 1884 -107.8937 46.98254 35 petroleum
## 1885 -107.9395 46.91378 35 petroleum
## 1886 -107.9338 46.89087 35 petroleum
## 1887 -107.8937 46.86222 35 petroleum
## 1888 -107.8765 46.84503 35 petroleum
## 1890 -108.3234 47.57269 36 phillips
## 1891 -108.3922 47.59560 36 phillips
## 1892 -108.4323 47.60706 36 phillips
## 1893 -108.4380 47.61279 36 phillips
## 1894 -108.4724 47.60133 36 phillips
## 1895 -108.5411 47.62998 36 phillips
## 1896 -108.6729 47.63571 36 phillips
## 1897 -108.7302 47.64717 36 phillips
## 1898 -108.7760 47.66436 36 phillips
## 1899 -108.7932 47.69301 36 phillips
## 1900 -108.8562 47.73312 36 phillips
## 1901 -108.8964 47.73884 36 phillips
## 1902 -108.8964 47.84771 36 phillips
## 1903 -108.8849 47.85917 36 phillips
## 1904 -108.8849 47.91646 36 phillips
## 1905 -108.6328 47.92219 36 phillips
## 1906 -108.6385 47.95084 36 phillips
## 1907 -108.6213 47.98521 36 phillips
## 1908 -108.5984 47.99667 36 phillips
## 1909 -108.5526 47.98521 36 phillips
## 1910 -108.4380 47.98521 36 phillips
## 1911 -108.4380 48.21440 36 phillips
## 1912 -108.4151 48.21440 36 phillips
## 1913 -108.4093 48.42066 36 phillips
## 1914 -108.3521 48.43213 36 phillips
## 1915 -108.3406 48.56390 36 phillips
## 1916 -108.3119 48.56390 36 phillips
## 1917 -108.3177 48.73006 36 phillips
## 1918 -108.2604 48.74152 36 phillips
## 1919 -108.2604 48.91341 36 phillips
## 1920 -108.2375 48.91914 36 phillips
## 1921 -108.2317 48.99362 36 phillips
## 1922 -108.0942 48.99935 36 phillips
## 1923 -107.8765 48.99362 36 phillips
## 1924 -107.6817 48.99362 36 phillips
## 1925 -107.4410 48.99935 36 phillips
## 1926 -107.2749 48.99362 36 phillips
## 1927 -107.1832 48.99935 36 phillips
## 1928 -107.1832 48.91914 36 phillips
## 1929 -107.2119 48.91341 36 phillips
## 1930 -107.2119 48.56390 36 phillips
## 1931 -107.1947 48.55244 36 phillips
## 1932 -107.2004 48.51234 36 phillips
## 1933 -107.2176 48.50661 36 phillips
## 1934 -107.2520 48.51807 36 phillips
## 1935 -107.2692 48.50661 36 phillips
## 1936 -107.2634 48.47796 36 phillips
## 1937 -107.3035 48.47796 36 phillips
## 1938 -107.3093 48.38629 36 phillips
## 1939 -107.2634 48.38055 36 phillips
## 1940 -107.2634 48.30034 36 phillips
## 1941 -107.3723 48.29461 36 phillips
## 1942 -107.3723 48.22013 36 phillips
## 1943 -107.4009 48.22013 36 phillips
## 1944 -107.4009 47.88208 36 phillips
## 1945 -107.4239 47.86489 36 phillips
## 1946 -107.4124 47.77895 36 phillips
## 1947 -107.4181 47.69301 36 phillips
## 1948 -107.4181 47.65863 36 phillips
## 1949 -107.4353 47.64717 36 phillips
## 1950 -107.4353 47.62998 36 phillips
## 1951 -107.4983 47.62425 36 phillips
## 1952 -107.5213 47.65290 36 phillips
## 1953 -107.6015 47.65290 36 phillips
## 1954 -107.6244 47.64144 36 phillips
## 1955 -107.6702 47.65290 36 phillips
## 1956 -107.6702 47.62425 36 phillips
## 1957 -107.7046 47.62425 36 phillips
## 1958 -107.7103 47.58987 36 phillips
## 1959 -107.7676 47.57269 36 phillips
## 1960 -107.7676 47.54977 36 phillips
## 1961 -107.8192 47.54977 36 phillips
## 1962 -107.8593 47.52685 36 phillips
## 1963 -107.8593 47.47528 36 phillips
## 1964 -107.9166 47.45810 36 phillips
## 1965 -107.9051 47.49820 36 phillips
## 1966 -107.9223 47.56123 36 phillips
## 1967 -107.9624 47.58987 36 phillips
## 1968 -108.0140 47.60133 36 phillips
## 1969 -108.0770 47.60133 36 phillips
## 1970 -108.2145 47.59560 36 phillips
## 1971 -108.3119 47.58987 36 phillips
## 1972 -108.3234 47.57269 36 phillips
## 1974 -112.1737 48.46077 37 pondera
## 1975 -112.1049 48.46077 37 pondera
## 1976 -112.0877 48.44931 37 pondera
## 1977 -112.0476 48.44931 37 pondera
## 1978 -112.0304 48.44358 37 pondera
## 1979 -111.9731 48.42066 37 pondera
## 1980 -111.9731 48.38055 37 pondera
## 1981 -111.7783 48.38055 37 pondera
## 1982 -111.7669 48.34045 37 pondera
## 1983 -111.6523 48.33472 37 pondera
## 1984 -111.6466 48.20867 37 pondera
## 1985 -111.3945 48.20867 37 pondera
## 1986 -111.3945 48.12273 37 pondera
## 1987 -111.4002 47.98521 37 pondera
## 1988 -111.9617 47.98521 37 pondera
## 1989 -111.9674 48.03678 37 pondera
## 1990 -112.0362 48.03678 37 pondera
## 1991 -112.0362 48.07689 37 pondera
## 1992 -112.1622 48.08262 37 pondera
## 1993 -112.1622 48.12273 37 pondera
## 1994 -112.5977 48.12845 37 pondera
## 1995 -112.9815 48.11700 37 pondera
## 1996 -113.0159 48.12273 37 pondera
## 1997 -113.0618 48.13418 37 pondera
## 1998 -113.1191 48.13418 37 pondera
## 1999 -113.1477 48.15710 37 pondera
## 2000 -113.2107 48.16283 37 pondera
## 2001 -113.2394 48.20294 37 pondera
## 2002 -113.2279 48.23732 37 pondera
## 2003 -113.2909 48.27169 37 pondera
## 2004 -113.3253 48.28315 37 pondera
## 2005 -113.3311 48.30034 37 pondera
## 2006 -112.5576 48.29461 37 pondera
## 2007 -112.5633 48.46650 37 pondera
## 2008 -112.1966 48.46650 37 pondera
## 2009 -112.1737 48.46077 37 pondera
## 2011 -104.9831 45.78506 38 powder river
## 2012 -104.9831 45.71630 38 powder river
## 2013 -104.9659 45.70484 38 powder river
## 2014 -104.9716 45.41837 38 powder river
## 2015 -104.9945 45.41837 38 powder river
## 2016 -104.9945 45.36107 38 powder river
## 2017 -105.0289 45.36107 38 powder river
## 2018 -105.0289 45.00583 38 powder river
## 2019 -105.0862 45.00010 38 powder river
## 2020 -105.1893 45.00583 38 powder river
## 2021 -105.3669 45.00583 38 powder river
## 2022 -105.6935 45.00583 38 powder river
## 2023 -105.9112 45.00583 38 powder river
## 2024 -106.0201 45.00010 38 powder river
## 2025 -106.2378 45.00583 38 powder river
## 2026 -106.2321 45.18918 38 powder river
## 2027 -106.2550 45.18918 38 powder river
## 2028 -106.2493 45.36107 38 powder river
## 2029 -106.2092 45.37253 38 powder river
## 2030 -106.2035 45.78506 38 powder river
## 2031 -106.1576 45.79079 38 powder river
## 2032 -106.0373 45.78506 38 powder river
## 2033 -105.3669 45.78506 38 powder river
## 2034 -105.3669 45.79652 38 powder river
## 2035 -105.1549 45.80798 38 powder river
## 2036 -105.1549 45.79079 38 powder river
## 2037 -104.9831 45.78506 38 powder river
## 2039 -113.2852 46.82784 39 powell
## 2040 -113.2967 47.17162 39 powell
## 2041 -113.4571 47.17162 39 powell
## 2042 -113.4571 47.59560 39 powell
## 2043 -113.1362 47.58987 39 powell
## 2044 -113.1305 47.55550 39 powell
## 2045 -113.0847 47.50966 39 powell
## 2046 -113.1248 47.48674 39 powell
## 2047 -113.0961 47.47528 39 powell
## 2048 -113.0446 47.48101 39 powell
## 2049 -113.0446 47.16589 39 powell
## 2050 -112.7925 47.15443 39 powell
## 2051 -112.7925 46.82784 39 powell
## 2052 -112.5289 46.82211 39 powell
## 2053 -112.5289 46.69033 39 powell
## 2054 -112.4086 46.69033 39 powell
## 2055 -112.4029 46.65022 39 powell
## 2056 -112.3284 46.64450 39 powell
## 2057 -112.2883 46.59866 39 powell
## 2058 -112.3169 46.55855 39 powell
## 2059 -112.2940 46.49553 39 powell
## 2060 -112.3284 46.43250 39 powell
## 2061 -112.3284 46.39812 39 powell
## 2062 -112.3456 46.39812 39 powell
## 2063 -112.3513 46.38666 39 powell
## 2064 -112.3685 46.38093 39 powell
## 2065 -112.4201 46.37521 39 powell
## 2066 -112.4430 46.34082 39 powell
## 2067 -112.4716 46.32364 39 powell
## 2068 -112.4945 46.33510 39 powell
## 2069 -112.5289 46.31791 39 powell
## 2070 -112.5346 46.28353 39 powell
## 2071 -112.5461 46.26061 39 powell
## 2072 -113.0274 46.25488 39 powell
## 2073 -113.0274 46.73044 39 powell
## 2074 -113.0446 46.73617 39 powell
## 2075 -113.1420 46.67887 39 powell
## 2076 -113.1764 46.72471 39 powell
## 2077 -113.1764 46.74763 39 powell
## 2078 -113.2222 46.78773 39 powell
## 2079 -113.2451 46.82784 39 powell
## 2080 -113.2852 46.82784 39 powell
## 2082 -106.0659 47.18880 40 prairie
## 2083 -105.9399 47.18307 40 prairie
## 2084 -105.9399 47.09713 40 prairie
## 2085 -105.8024 47.09713 40 prairie
## 2086 -105.7967 47.18307 40 prairie
## 2087 -105.4013 47.18880 40 prairie
## 2088 -105.3154 47.18307 40 prairie
## 2089 -105.3096 46.98254 40 prairie
## 2090 -105.1836 46.97681 40 prairie
## 2091 -105.1836 46.91951 40 prairie
## 2092 -105.0690 46.91951 40 prairie
## 2093 -105.0174 46.89660 40 prairie
## 2094 -105.0174 46.86222 40 prairie
## 2095 -104.5934 46.86222 40 prairie
## 2096 -104.5877 46.68460 40 prairie
## 2097 -104.5877 46.66168 40 prairie
## 2098 -104.7138 46.65595 40 prairie
## 2099 -104.7195 46.62158 40 prairie
## 2100 -104.8513 46.62158 40 prairie
## 2101 -104.8513 46.57574 40 prairie
## 2102 -104.9831 46.57574 40 prairie
## 2103 -104.9831 46.55282 40 prairie
## 2104 -105.2237 46.54136 40 prairie
## 2105 -105.2294 46.57574 40 prairie
## 2106 -105.4357 46.57001 40 prairie
## 2107 -105.4357 46.60439 40 prairie
## 2108 -105.4701 46.60439 40 prairie
## 2109 -105.4815 46.65022 40 prairie
## 2110 -105.5446 46.66741 40 prairie
## 2111 -105.5503 46.74763 40 prairie
## 2112 -105.5847 46.74763 40 prairie
## 2113 -105.5904 46.82784 40 prairie
## 2114 -105.8024 46.84503 40 prairie
## 2115 -105.8081 46.87368 40 prairie
## 2116 -106.0659 46.87368 40 prairie
## 2117 -106.0659 47.18880 40 prairie
## 2119 -113.8009 46.65595 41 ravalli
## 2120 -113.8123 46.63303 41 ravalli
## 2121 -113.7894 46.61012 41 ravalli
## 2122 -113.7951 46.57574 41 ravalli
## 2123 -113.8123 46.57001 41 ravalli
## 2124 -113.8238 46.52990 41 ravalli
## 2125 -113.7951 46.51271 41 ravalli
## 2126 -113.8353 46.48407 41 ravalli
## 2127 -113.8353 46.41531 41 ravalli
## 2128 -113.7894 46.39240 41 ravalli
## 2129 -113.7608 46.30645 41 ravalli
## 2130 -113.7321 46.26634 41 ravalli
## 2131 -113.7722 46.23769 41 ravalli
## 2132 -113.7722 46.20905 41 ravalli
## 2133 -113.8066 46.20905 41 ravalli
## 2134 -113.8066 46.19759 41 ravalli
## 2135 -113.7722 46.18040 41 ravalli
## 2136 -113.7780 46.12883 41 ravalli
## 2137 -113.7379 46.10019 41 ravalli
## 2138 -113.7837 46.04289 41 ravalli
## 2139 -113.7665 46.02570 41 ravalli
## 2140 -113.7321 46.04289 41 ravalli
## 2141 -113.6691 46.02570 41 ravalli
## 2142 -113.6519 45.97986 41 ravalli
## 2143 -113.6061 45.93402 41 ravalli
## 2144 -113.5373 45.90538 41 ravalli
## 2145 -113.5946 45.88246 41 ravalli
## 2146 -113.6920 45.85381 41 ravalli
## 2147 -113.7379 45.83089 41 ravalli
## 2148 -113.7722 45.80798 41 ravalli
## 2149 -113.7837 45.76787 41 ravalli
## 2150 -113.8066 45.74495 41 ravalli
## 2151 -113.8524 45.75068 41 ravalli
## 2152 -113.8811 45.75068 41 ravalli
## 2153 -113.8925 45.72776 41 ravalli
## 2154 -113.9327 45.69911 41 ravalli
## 2155 -113.9900 45.70484 41 ravalli
## 2156 -114.0702 45.61317 41 ravalli
## 2157 -114.1618 45.53296 41 ravalli
## 2158 -114.2306 45.53296 41 ravalli
## 2159 -114.2650 45.47566 41 ravalli
## 2160 -114.3337 45.45847 41 ravalli
## 2161 -114.4483 45.52723 41 ravalli
## 2162 -114.4655 45.55587 41 ravalli
## 2163 -114.5400 45.56160 41 ravalli
## 2164 -114.5629 45.62463 41 ravalli
## 2165 -114.4942 45.65900 41 ravalli
## 2166 -114.4999 45.69339 41 ravalli
## 2167 -114.5171 45.73349 41 ravalli
## 2168 -114.5400 45.76787 41 ravalli
## 2169 -114.5285 45.81371 41 ravalli
## 2170 -114.4655 45.84808 41 ravalli
## 2171 -114.3738 45.87673 41 ravalli
## 2172 -114.4082 45.92257 41 ravalli
## 2173 -114.4082 45.97413 41 ravalli
## 2174 -114.4598 45.99132 41 ravalli
## 2175 -114.4999 46.03716 41 ravalli
## 2176 -114.4598 46.07153 41 ravalli
## 2177 -114.4540 46.09445 41 ravalli
## 2178 -114.5056 46.12883 41 ravalli
## 2179 -114.5056 46.15748 41 ravalli
## 2180 -114.4426 46.16894 41 ravalli
## 2181 -114.4369 46.20905 41 ravalli
## 2182 -114.4598 46.25488 41 ravalli
## 2183 -114.4369 46.27207 41 ravalli
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## 2640 -111.4002 47.98521 50 teton
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## 2666 -112.0935 48.99362 51 toole
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## 2671 -111.2512 48.90195 51 toole
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## 2678 -107.5098 46.04289 52 treasure
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## 2691 -107.1603 46.48407 52 treasure
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## 2694 -107.0171 46.31218 52 treasure
## 2695 -106.9884 46.31218 52 treasure
## 2696 -106.9827 46.14029 52 treasure
## 2697 -106.9254 46.14029 52 treasure
## 2698 -106.9311 45.86527 52 treasure
## 2699 -107.0342 45.86527 52 treasure
## 2700 -107.0400 45.91684 52 treasure
## 2701 -107.1660 45.91684 52 treasure
## 2702 -107.1718 45.93976 52 treasure
## 2703 -107.4124 45.95694 52 treasure
## 2704 -107.4181 46.04289 52 treasure
## 2705 -107.5098 46.04289 52 treasure
## 2707 -105.8310 48.02532 53 valley
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## 2709 -105.8998 48.02532 53 valley
## 2710 -105.9857 48.04251 53 valley
## 2711 -106.0201 48.03678 53 valley
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## 2717 -106.1404 48.04251 53 valley
## 2718 -106.3868 48.07689 53 valley
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## 2721 -106.4097 47.96230 53 valley
## 2722 -106.4842 47.96230 53 valley
## 2723 -106.4727 47.93938 53 valley
## 2724 -106.4957 47.92219 53 valley
## 2725 -106.4842 47.89927 53 valley
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## 2731 -106.7993 47.75603 53 valley
## 2732 -106.8795 47.73312 53 valley
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## 2748 -107.2634 48.47796 53 valley
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## 2770 -105.7852 48.41494 53 valley
## 2771 -105.7909 48.22586 53 valley
## 2772 -105.8253 48.22013 53 valley
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## 2775 -110.2772 46.67887 54 wheatland
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## 2791 -110.2829 46.21478 54 wheatland
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## 2794 -104.5934 46.86222 55 wibaux
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## 2810 -104.3356 46.64450 55 wibaux
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## 2815 -104.5934 46.86222 55 wibaux
## 2817 -107.7791 46.48980 56 yellowstone
## 2818 -107.7504 46.49553 56 yellowstone
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## 2835 -107.8307 45.96840 56 yellowstone
## 2836 -107.8364 45.93976 56 yellowstone
## 2837 -107.8765 45.93402 56 yellowstone
## 2838 -107.8822 45.90538 56 yellowstone
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## 2843 -108.1973 45.52150 56 yellowstone
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## 2848 -108.6557 45.46993 56 yellowstone
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## 2858 -108.8620 45.64755 56 yellowstone
## 2859 -108.8849 45.66473 56 yellowstone
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## 2861 -108.9136 45.69339 56 yellowstone
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## 2868 -108.4093 46.12883 56 yellowstone
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## 2889 -111.0564 44.49017 57 yellowstone national
## 2890 -111.0908 44.49017 57 yellowstone national
## 2891 -111.0965 44.89697 57 yellowstone national
## 2892 -111.0965 45.04021 57 yellowstone national
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## 2898 -111.0163 45.07459 57 yellowstone national
## 2899 -110.9991 45.08605 57 yellowstone national
## 2900 -110.9647 45.09751 57 yellowstone national
## 2901 -110.9590 45.07459 57 yellowstone national
## 2902 -110.9304 45.06313 57 yellowstone national
## 2903 -110.9189 45.03448 57 yellowstone national
## 2904 -110.8673 45.02875 57 yellowstone national
## 2905 -110.8731 45.04594 57 yellowstone national
## 2906 -110.8387 45.06313 57 yellowstone national
## 2907 -110.8158 45.05167 57 yellowstone national
## 2908 -110.8043 45.03448 57 yellowstone national
## 2909 -110.5236 45.03448 57 yellowstone national
## 2910 -110.1225 45.02875 57 yellowstone national
## 2911 -110.0308 45.04021 57 yellowstone national
## 2912 -110.0308 45.05167 57 yellowstone national
## 2913 -110.0022 45.05167 57 yellowstone national
## 2914 -109.9735 45.04021 57 yellowstone national
## 2915 -109.9850 45.01157 57 yellowstone national
## 2916 -109.9964 44.99438 57 yellowstone national
## 2917 -111.0450 44.99438 57 yellowstone national
## 2918 -111.0564 44.49017 57 yellowstone nationalMontana Maps
mt <- ggplot(mt_counties, aes(lon, lat, group = group)) +
geom_polygon(fill = "white", colour = "grey50") +
coord_quickmap()
mt
Montana Maps
mt + annotate('text',label = 'good guys', x = -111, y = 45.67, col = "darkblue") +
annotate('point', x = -111, y = 46, col = "darkblue") +
annotate('text',label = 'bad guys', x = -114, y = 47, col = "darkred") +
annotate('point', x = -114, y = 46.8, col = "darkred")
ggmap
The ggmap package contains nice tools for creating maps; however, it requries creating a google api to fetch the maps.
https://github.com/dkahle/ggmap
leaflet
Leaflet is another good option https://rstudio.github.io/leaflet/, however, it does not work well with this html slide conversion.
tmap
tmap also uses the grammar of graphics (gg) principles. https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html
## Simple feature collection with 177 features and 15 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -16656120 ymin: -8460601 xmax: 16656120 ymax: 8375779
## epsg (SRID): NA
## proj4string: +proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
## First 10 features:
## iso_a3 name sovereignt continent
## 1 AFG Afghanistan Afghanistan Asia
## 2 AGO Angola Angola Africa
## 3 ALB Albania Albania Europe
## 4 ARE United Arab Emirates United Arab Emirates Asia
## 5 ARG Argentina Argentina South America
## 6 ARM Armenia Armenia Asia
## 7 ATA Antarctica Antarctica Antarctica
## 8 ATF Fr. S. Antarctic Lands France Seven seas (open ocean)
## 9 AUS Australia Australia Oceania
## 10 AUT Austria Austria Europe
## area pop_est pop_est_dens economy
## 1 652860.000 [km^2] 28400000 4.350090e+01 7. Least developed region
## 2 1246700.000 [km^2] 12799293 1.026654e+01 7. Least developed region
## 3 27400.000 [km^2] 3639453 1.328268e+02 6. Developing region
## 4 71252.172 [km^2] 4798491 6.734519e+01 6. Developing region
## 5 2736690.000 [km^2] 40913584 1.495003e+01 5. Emerging region: G20
## 6 28470.000 [km^2] 2967004 1.042151e+02 6. Developing region
## 7 12259213.973 [km^2] 3802 3.101341e-04 6. Developing region
## 8 7257.455 [km^2] 140 1.929051e-02 6. Developing region
## 9 7682300.000 [km^2] 21262641 2.767744e+00 2. Developed region: nonG7
## 10 82523.000 [km^2] 8210281 9.949082e+01 2. Developed region: nonG7
## income_grp gdp_cap_est life_exp well_being footprint inequality
## 1 5. Low income 784.1549 59.668 3.8 0.79 0.42655744
## 2 3. Upper middle income 8617.6635 NA NA NA NA
## 3 4. Lower middle income 5992.6588 77.347 5.5 2.21 0.16513372
## 4 2. High income: nonOECD 38407.9078 NA NA NA NA
## 5 3. Upper middle income 14027.1261 75.927 6.5 3.14 0.16423830
## 6 4. Lower middle income 6326.2469 74.446 4.3 2.23 0.21664810
## 7 2. High income: nonOECD 200000.0000 NA NA NA NA
## 8 2. High income: nonOECD 114285.7143 NA NA NA NA
## 9 1. High income: OECD 37634.0832 82.052 7.2 9.31 0.08067825
## 10 1. High income: OECD 40132.6093 81.004 7.4 6.06 0.07129351
## HPI geometry
## 1 20.22535 MULTIPOLYGON (((5310471 451...
## 2 NA MULTIPOLYGON (((1531585 -77...
## 3 36.76687 MULTIPOLYGON (((1729835 521...
## 4 NA MULTIPOLYGON (((4675864 313...
## 5 35.19024 MULTIPOLYGON (((-5017766 -6...
## 6 25.66642 MULTIPOLYGON (((3677241 513...
## 7 NA MULTIPOLYGON (((-3403793 -8...
## 8 NA MULTIPOLYGON (((5550200 -59...
## 9 21.22897 MULTIPOLYGON (((12288137 -5...
## 10 30.47822 MULTIPOLYGON (((1371802 588...tmap
gg add on packages
more gg packages
Another of my favorite packages is the ggforce package: https://github.com/thomasp85/ggforce https://www.r-bloggers.com/the-ggforce-awakens-again/
Here is a link to other gg add ons http://www.ggplot2-exts.org/gallery/
Exercise
Using either the Seattle Housing dataset or the billboard data, explore the packages above.
Solution
